Language : English
周谧

Paper Publications

Cardinal-Ordinal Consistency Adjustment Driven Group Consensus Decision Making With Distributed Preference Relations and Application in Remanufacturing Quality Management

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DOI number:10.1109/TCSS.2026.3694094

Affiliation of Author(s):Hefei University of Technology

Journal:IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS

Key Words:Adjustment model, cardinal and ordinal consistency, consensus reaching process (CRP), distributed preference relation (DPR), group decision making (GDM)

Abstract:The consistency of expert’s assessment and consensus reaching process (CRP) is a hot topic in group decision making (GDM). When an expert provides the comparison between different alternative pairs, how to measure the consistency degree and adjust inconsistent judgments is important. In this article, the construction of complete cardinal consistent distributed preference relations (DPRs) from multiple routes is first proposed. The advantage lies in that all possible results could be captured instead of just depending on adjacent alternative pairs. Then, the ordinal consistency measurement of DPRs is provided based on the ranking order dissimilarity derived from possibility degree. Second, the concepts of soft and strict consistent DPRs are given, which deduce four consistency types, i.e., perfect consistent, partial consistent, partial inconsistent, and completely inconsistent. We also discuss the modeling of consistent DPR matrix when adding new alternatives. Third, several optimization models are established to adjust inconsistent DPRs under different consistency types. These models all minimize the modification extent of individual's original judgment. Ordinal consensus index is then defined, followed by the optimization model to make experts reach ordinal consensus while maintaining cardinal and ordinal consistency. A case study about the selection of a dismantling scheme in remanufacturing quality management process is finally provided to illustrate the effectiveness and validity of the proposed method.

Co-author:Mi Zhou*,Bayi Cheng,Jian Wu,Xinbao Liu

First Author:Wanlin Liu

Indexed by:Journal paper

Discipline:Management Science

Document Type:J

Volume:13

Issue:4

Page Number:4469-4481

Translation or Not:no

Date of Publication:2026-06-04

Included Journals:SCI